{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Chipotle's Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "# set this so the \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable called chipo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "    \n",
    "chipo = pd.read_csv(url, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},

   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the Series of the names\n",
    "x = chipo.item_name\n",
    "\n",
    "# use the Counter class from collections to create a dictionary with keys(text) and frequency\n",
    "letter_counts = Counter(x)\n",
    "\n",
    "# convert the dictionary to a DataFrame\n",
    "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
    "\n",
    "# sort the values from the top to the least value and slice the first 5 items\n",
    "df = df[0].sort_values(ascending = True)[45:50]\n",
    "\n",
    "# create the plot\n",
    "df.plot(kind='bar')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Items')\n",
    "plt.ylabel('Number of Times Ordered')\n",
    "plt.title('Most ordered Chipotle\\'s Items')\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
    "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 36.7178857951459)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SV8nXuiZ1MXvq7HH3UclUz9kk6+k8DhARdwCzxt2zmZnton9uP20qnZrb1Eb/If3j7qOSxL8tIv487p7MzGxUPZN7WHLSEno6e5468++a1EVPZ9Le3dk97j4qKedcIenvgXZJc4APAr8cd89mZlbS/JnzGTh9gMUrFrN6w2pmT51N/yH9NUn6UFni/wDwcWAI+DpwA3BeTXo3M7OSuju7WTRvUSbHHjHxS2oHzo2Ij5IkfzMzm+BGnOOPiO3A4XWKxczM6qCSqZ7fpuWb1wBPFZcO31HLzMwmlkoS/1RgPfCKgrYAnPjNzCagSj656+UZzMxaSCWf3H2upJslrUifv0DSP2UfmpmZZaGSD3BdDJwFbAWIiLuAt2YZlJmZZaeSOf7dI+LX0k63z92WUTxmLSfLG2qYVaOSxP+YpANILugi6c3A2tF2krQ/cCWwD7ADuCgiPi9pKrCYZL2fNcBbIuJPVUVv1uSyvqGGWTUqmep5H/CfwEGS/kiyJv//qmC/bcDpEfE84CjgfZIOBs4Ebo6IOcDN6XOzllOPG2qYVWPUxB8RD0TEsUAvyW0Y50fEmgr2WxsRy9PHgyR37ZoBvBG4It3sCuBNVcZu1tTqcUMNs2qUneqRdFqZdgAi4nOVdiJpFnAYcBuwd0SsTY+xVtIzy+xzCnAKwMyZMyvtyqxp1OOGGmbVGOmMvyf96iOZ2pmRfv0jcHClHUjqBr4NnBoRGyvdLyIuioi+iOjr7e2tdDezplGPG2qYVaPsGX9EnAsg6UZgXjpdg6RzSJZvGJWkSSRJ/6qCJR4ekTQ9PdufDjw6jvitybiC5Wn9c/s57YaSfzjX7IYaZtWopKpnJrCl4PkWKrgDl5I5oUuBe4umha4DFgIXpP9+r9Jgrbm5gmVnwzfUKB6TNrXV7IYaZtVQRIy8gfRx4C3AtSQlnccDiyPi/FH2mw/8HLibpJwT4GMk8/zfJPmF8iDwdxGxYaRj9fX1xbJly0Z9M9Y4g0ODzPjcDAa3DO7yWk9nDwOnD+Q20W3asimzG2qYjUTS7RHRV9xeyVo9n5J0PfDStOkdEfHbCvZbCqjMy68cbX+bWCqpYMnqphLNLssbaphVY7QbsbQBd0XEIcDy+oRkE5ErWMwmjtFuxLIDuFOS6yltRK5gMZs4Krm4Ox1YKenX7HwjljdkFpVNOPWsYHHlkNn4VJL4z808Cpvw6lXB4sohs/EbtaoHQNLewBHp019HRF1r713VM3FkWcHiyiGzsam6qkfSW4B/AW4hqdL5d0kfjYhv1TxKm/CyrGBx5ZBZbVQy1fNx4Ijhs3xJvcCPASd+qytXDpnVRiXLMrcVTe2sr3A/s5py5ZBZbVSSwH8k6QZJJ0s6GfghsCTbsMx21T+3nzaV/pHNeu2bwaFBLll+CWfcdAaXLL+EwaFdrzOYTRSVXtw9AZhPMsf/s4i4NuvACvnirg0rVdUzXDmUVVVPI/o0q4VyF3crSvyN5sRvheq59o0riWwiq7qqx6zZ1HPtG1cSWSvyRVqzEbiSyFrRmBK/pD0lvSCrYMyajSuJrBWNmvgl3SLpGZKmAncCX5FU8f12zarVDJU0jawkMstKJXP8fxURGyW9C/hKRJwt6a6sA7N8a5Y1eXwXLWtFlST+jvTeuG8h+RSvWaYGhwZZcNWCnSpphufZF1y1oO6VNPNnzmfg9AHfRctaRiWJ/xPADcDSiPiNpOcAq7INy/KsGStpfBctayWV3HrxGuCagucPAH+bZVCWb66kMctWJatzPhv4ADCrcHvfiMWyMlxJUyr5u5LGbPwqmer5LnAp8H2g9N/fZjVUz7t5meVRJXX8T0bEv0XETyPi1uGv0XaSdJmkRyWtKGg7R9IfJd2Rfi0YV/TWkoYraXo6e56qoe+a1EVPZ48racxqYNS1eiT9PTAHuBEYGm6PiOWj7Hc0sAm4MiIOSdvOATZFxL+OJUiv1ZNP9VyTx6wVjWetnucDbwNewdNTPZE+LysifiZp1tjCNHuaK2nMslFJ4j8eeE5EbKlRn++X9HZgGXB6RPyp1EaSTgFOAZg5c2aNujYzs0rm+O8E9qhRf18CDgAOBdYCny23YURcFBF9EdHX29tbo+7NzKySM/69gfsk/Yad5/jHXM4ZEY8MP5Z0MfCDsR7DzGpjcGiQxSsXs2r9KubsNYf+uf30TO5pdFhWB5Uk/rNr1Zmk6RGxNn16PLBipO3NLBvNshaSNUYln9y9VdKzgDkR8WNJuwPto+0n6WrgGGCapD+Q/AI5RtKhJBeH1wDvGUfsZlaFZlsLyeqvkk/uvpvkIutUkvn5GcCXgVeOtF9EnFii+dIqYjSzGmrGtZCsviq5uPs+4CXARoCIWAU8M8ugzCw7XgvJKkn8Q4WlnJI6SKZqzGwC8l3FrJLEf6ukjwG7SXoVyUqd3882LDPLiu8qZpUk/jOBdcDdJBdjl0SEb8hiNkF5LSSrpJzzAxHxeeDi4QZJH0rbzGwC8l3F8q2SRdqWR8S8orbfRsRhmUZWwIu0mZmN3ZgXaZN0IvD3wLMlXVfwUg+wvvYhmplZPYw01fNLkvV0prHzmjqDwF1ZBmVmZtkpm/gj4vfA74EX1y8cs4nJ697YRDLSVM8gpev1BUREPCOzqMwmEK97YxPNSGf8Pl0xG4XXvbGJqJI6fjMro5J1b8yajRO/2Th43RubiJz4zcbB697YRFTJJ3fNxqRZK1yyiKt/bj+n3XBayddGW/emWcfJWt+on9xtBv7k7sRRqsKlTW0Nr3DJMq5qjt2s42Stpdwnd534rWYGhwaZ8bkZO1W4DOvp7GlYhUs94tq0ZVPF69406zhZ6ymX+D3HbzXTrBUu9Yiru7ObRfMWcf6x57No3qIRE3ezjpPlhxO/1UyzVrg0W1zNFo/ljxO/1UyzVrg0W1zNFo/lT2aJX9Jlkh6VtKKgbaqkmyStSv/dM6v+LTE4NMglyy/hjJvO4JLllzA4tOu8cq1kfWenat9LNXFlOW6+A5Y1WmYXdyUdDWwCroyIQ9K2zwAbIuICSWcCe0bEGaMdyxd3q9OIypGs+hzvcceyfz3GzVU9Vg8NqeqRNAv4QUHivx84JiLWSpoO3BIRB452HCf+sWtk5chYKlwqUav3Uklc9Ry3Wo+TWbEx34glI3tHxFqANPk/s87950YllSOL5i3KpO/hCpdaqdV7qSSueo5brcfJrFJNe3FX0imSlklatm7dukaHM+G0UuVIPd9LK42bWTn1TvyPpFM8pP8+Wm7DiLgoIvoioq+3t7duAbaKVqocqed7aaVxMyun3on/OmBh+ngh8L06958bjawcqXVFTD3fiytuLA+yLOe8Gvgv4EBJf5C0CLgAeJWkVcCr0ueWgZ7JPSw5aQk9nT1PncF2TeqipzNpz+oi4tIHlzLjczM49Uen8plffoZTf3QqMz43g6UPLq36mPV8L40aN7N68lo9La6elSNZV8TU87244sZaQbNU9Vid1bNyJOuKmHq+F1fcWCtr2qoem3hcEWM2MfiMf5yquZlGljfgKD72gtkLWLJ6SV1u9jFcEVMq+e/esXvFFTEDGwc46+azuO+x+zho2kGc/8rz2fcZ+9Y6XLPc8hz/ODTbDTiKjz2lYwpPbnuSKe1TeHL7k5kvCzDSHD/ADf9wA8cdcNyIx/jib77I+5a8b5f2Lyz4Au894r01idMsL3wjlhqr5kJmlhc/R0u6texrJDf+7kZe/bVXV9XvwMYBZlw4o+yx156+ln2696lJnGZ54Bux1Fg1N9PI8gYcIx271n2N5ME/P1j2A1Cj9XvWzWeNeOwzf3zmuGIzs4QTf5WquZCZ5cXPkY5d676qjWO0fu977L4Rj33/Y/ePKzYzSzjxV6maj/ZnuRzASMeudV/VxjFavwdNO2jEYx84bdSFXM2sAk78VRrpo/1bt2/liW1P7LRUweDQIE9ue5Kt27eW3KdNbSyYs2CXpQ6Klz8Y2DhQcjmEkeIpNrRtiItuv4i+i/pYeO1CBjYOPBXjeJdaGM+SB+e/8vwRj33Bsf6gt1kt+OLuOBRX0RQqrKABRt3ugmMv4Mwfn7lTtc+O2IEkhCqq0hmtqmckHz7qw1yy/JKaVBuNp3LJVT1mteOqnoxs2rKJK+64gg/f8GG27tj1bL57Ujco2a7YpLZJXPiaCznhoBM48D8OrKgip5TCapnipQb+5rl/w7fv+XbZ+MZy7LEYz5IHD296mDN/fCb3P3Y/B047kAuOvcDVPGZV8JINGenu7GZyx2Q62ztLJtYtO7YgVHLfzvZOprRP4YerflhxRU4phcshlFpqYKT4xnLssRjPkgf7dO/D5W+6vKp9zWx0Tvw1MFIly5btW8ruN1zlUmoKaCxGq5YZS8XPWI9tZhOPE38NjLRUQWd7J0IMbR/a5bXhKpcgyu5fidGqZUaKb7zHNrOJx4m/hHJr6Qy3r3x0JRue2PDUnHpXZxfbY3vJY23ZXn6qZ3tsZ8WjKzhg6gEwjkst23Zs4+oVV3PNPdfwuue+joUvXMjg0CBn3XwWK9etpF3tDG3d9RdPJcpV4mS53pCZZcsXd4uUq0gZrrrZumMrT27btUKms62TLTu20NneOeL0Tql9Kqm6GYuOtg627dg2pn3GUtWT5XpDZlY7ruqpwFjWu5nIOtTB4dMPZwc7OLj34KeqZiqpxMn6ZitmVjuu6qnAWNa7mcgmd0zm3Ye/e5eqm0oqcbK+2YqZZc+f3C0wnuqXiWQ8lTq+2YrZxOfEX2As691MZOOp1MlyvSEzq49cT/UMV6aseHQFax5fw68e+lUuzvhHWzNnJP1z+znthtNqflwzq5+GJH5Ja4BBYDuwrdTFh6wNV6Zs3b61phU1zayzvZPJ7ZNZctKSqi/A9kzuYclJS8pW9fjCrlnza+QZ/8sj4rFGdDw4NMiCqxa0fPXOsDa18a557+LIfY8c05o55cyfOZ+B0weqXovHzBorl1M9eaneGbZbx24cue+RNa22Gc9aPGbWWI26uBvAjZJul3RKqQ0knSJpmaRl69atq2nneaneGeZqGzMr1KjE/5KImAe8FnifpKOLN4iIiyKiLyL6ent7a9p5Xqp3hrnaxswKNWSqJyIG0n8flXQtcCTwsyz6Gtg4wEdu+gjXr7qex4cez6KLpudqGzMrVPfEL6kLaIuIwfTxccAnsuir3N2c8mJKxxQmtU1ytY2Z7aQRZ/x7A9dKGu7/6xHxo1p3MrBxIDdJv13tfPKYT/KM3Z7BAxseYP0T69lr9704eNrBrrYxs13UPfFHxAPAC7Pu56ybz8q6i6YxpWMKz+x+pqtszKwiLbtkw32P3dfoEOrGVTtmNhYtm/gPmnZQo0OoG1ftmNlYtGziP/+V5zc6hLpx1Y6ZjUXLfnL3mCuOaXQImavF2jtmlj8tmfivvedaVm1Y1egwxqyDDraxDSGmd0/n8H0P5+FND9NGGx1tHax5fA3tbe1M757OEfsdwaF7H+qqHTMbs5ZM/Cd+58RGh1CVI/c7kl8s+kWjwzCzFteSc/xD24caHUJV1jy+ptEhmFkOtGTin9w+udEhVGXWHrMaHYKZ5UBLJv6rT7i60SFU5crjr2x0CGaWAy2Z+E+45oRGhzBmHz7qwxww9YBGh2FmOdByF3d1rhodwojaaaers4uO9g461MHsqbO58vgrnfTNrG5aLvE3ysIXLuTyN13e6DDMzEbVklM9jXD/Y/c3OgQzs4o48dfIgdMObHQIZmYVceKvkQuOvaDRIZiZVaTlEn+cHXXv8wsLvsA+3fvUvV8zs2q0XOIH2Hjmxrr0c1jvYaw9fS3vPeK9denPzKwWWrKqZ/HKxRVt14i/DszMGq0lz/hXrZ94K3OamdVLSyb+OXvNaXQIZmZNqyUTf//c0e9GtRu71SESM7Pm05DEL+k1ku6XtFrSmbU+fs/kHn7+jp+PuM1fzv5Lrbs1M5sQ6p74JbUDXwBeCxwMnCjp4Fr3M3/mfAbPGtylfTd280VdM8u1RlT1HAmsjogHACR9A3gjcE+tO+ru7HaSNzMr0ojEPwN4qOD5H4AXFW8k6RTglPTpJknNuBjONOCxRgdRhmOrjmOrjmOrTtaxPatUYyMSf6l1k3c5LY6nxDQAAAb+SURBVI+Ii4CLsg+nepKWRURfo+MoxbFVx7FVx7FVp1GxNeLi7h+A/Que7wcMNCAOM7NcakTi/w0wR9KzJXUCbwWua0AcZma5VPepnojYJun9wA1AO3BZRKysdxw10sxTUY6tOo6tOo6tOg2JTRGuejEzy5OW/OSumZmV58RvZpYzTvwVkrS/pJ9KulfSSkkfStvPkfRHSXekXwsaFN8aSXenMSxL26ZKuknSqvTfPRsQ14EFY3OHpI2STm3UuEm6TNKjklYUtJUcJyX+LV1a5C5J8xoQ279Iui/t/1pJe6TtsyQ9UTB+X25AbGW/h5LOSsftfkmvbkBsiwviWiPpjrS93uNWLm809mcuIvxVwRcwHZiXPu4B/ptkyYlzgI80QXxrgGlFbZ8Bzkwfnwl8usExtgMPk3yopCHjBhwNzANWjDZOwALgepLPnhwF3NaA2I4DOtLHny6IbVbhdg0at5Lfw/T/xZ3AZODZwO+A9nrGVvT6Z4H/26BxK5c3Gvoz5zP+CkXE2ohYnj4eBO4l+RRyM3sjcEX6+ArgTQ2MBeCVwO8i4veNCiAifgZsKGouN05vBK6MxK+APSRNr2dsEXFjRGxLn/6K5HMvdVdm3Mp5I/CNiBiKiP8BVpMs1VL32CQJeAtwdVb9j2SEvNHQnzkn/ipImgUcBtyWNr0//bPsskZMp6QCuFHS7elyFwB7R8RaSH4AgWc2KLZhb2Xn/4DNMG5QfpxKLS/SyF/27yQ5Gxz2bEm/lXSrpJc2KKZS38NmGreXAo9EROHdmRoybkV5o6E/c078YySpG/g2cGpEbAS+BBwAHAqsJfmzshFeEhHzSFY9fZ+koxsUR0lKPqz3BuCatKlZxm0kFS0vUg+SPg5sA65Km9YCMyPiMOA04OuSnlHnsMp9D5tm3IAT2flkoyHjViJvlN20RFvNx86JfwwkTSL55l0VEd8BiIhHImJ7ROwALibDP2lHEhED6b+PAtemcTwy/Gdi+u+jjYgt9VpgeUQ8As0zbqly49QUy4tIWgi8Djgp0ongdBplffr4dpJ59OfWM64RvofNMm4dwAnAUzfhbsS4lcobNPhnzom/Qulc4aXAvRHxuYL2wvm344EVxfvWIbYuST3Dj0kuCK4gWQpjYbrZQuB79Y6twE5nXs0wbgXKjdN1wNvTSoujgD8P/3leL5JeA5wBvCEi/lLQ3qvk3hZIeg4wB3igzrGV+x5eB7xV0mRJz05j+3U9Y0sdC9wXEX8Ybqj3uJXLGzT6Z65eV7cn+hcwn+RPrruAO9KvBcBXgbvT9uuA6Q2I7TkkVRR3AiuBj6ftewE3A6vSf6c2aOx2B9YDf1XQ1pBxI/nlsxbYSnJ2tajcOJH82f0FkrPCu4G+BsS2mmTOd/hn7svptn+bfq/vBJYDr29AbGW/h8DH03G7H3htvWNL2y8H/rFo23qPW7m80dCfOS/ZYGaWM57qMTPLGSd+M7OcceI3M8sZJ34zs5xx4jczyxknfms5kvaT9L105cPfSfp8+snhSva9XNKbx9H3yZLWpSs/3iPp3WW265P0b9X2YzYeTvzWUtIPzHwH+G5EzCH5VGY38KkS24771qNljrE4Ig4FjgH+WdLexftExLKI+OB4+zerhhO/tZpXAE9GxFcAImI78GHgnZJ2T8/Ir5H0fZJF7STpP9Kz8x9SsJCdpMPThbxul3RDwUfsb5H0z5JuBT5ULpBIls/4HfAsJWvXXyTpRuBKScdI+kF6vG5JX1FyP4W7JP1t2n6cpP+StDyNuTuTEbPcqfvN1s0yNhe4vbAhIjZKehCYnTa9GHhBRGyQdAJwIPB8YG/gHuCydH2VfwfeGBHrJPWT/NXwzvQYe0TEy0YKJF0S4Dkkn74FOByYHxFPSDqmYNP/Q/LR/Oen++0paRrwT8CxEbFZ0hkki4p9YozjYbYLJ35rNaL0aoaF7TdFxPD67UcDV6d/GQxI+knafiBwCHBTMntEO8myAMMWU16/pPnAEPCe9BcMwHUR8USJ7Y8lWbIagIj4k6TXkdyw4xfpvp3Af43Qp1nFnPit1awkWY/lKemyu/uTTLscDmwu2qfcL4qVEfHiMv0UH6PQ4oh4/xj2KfXLSiS/oE4coR+zqniO31rNzcDukt4OkK7E+Fng8ihY3bLAz0hWkmxP5/BfnrbfD/RKenF6nEmS5mYU843AU78olNzQ5FfASyTNTtt2l1TXZZetdTnxW0uJZNXB44G/k7SK5B6nTwIfK7PLtSQrJN5NcmORW9PjbAHeDHxa0p0kqyr+dUZhnwfsKWlF2tfLI2IdcDJwtaS7SH4RHJRR/5YzXp3TzCxnfMZvZpYzTvxmZjnjxG9mljNO/GZmOePEb2aWM078ZmY548RvZpYz/x9hN2BtC1PUZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a list of prices\n",
    "chipo.item_price = [float(value[1:-1]) for value in chipo.item_price] # strip the dollar sign and trailing space\n",
    "\n",
    "# then groupby the orders and sum\n",
    "orders = chipo.groupby('order_id').sum()\n",
    "\n",
    "# creates the scatterplot\n",
    "# plt.scatter(orders.quantity, orders.item_price, s = 50, c = 'green')\n",
    "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.xlabel('Order Price')\n",
    "plt.ylabel('Items ordered')\n",
    "plt.title('Number of items ordered per order price')\n",
    "plt.ylim(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create a question and a graph to answer your own question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
